System and methods for generating quality, verified, and synthesized information

ABSTRACT

An improved system and methods for identifying, assessing, obtaining, evaluating, processing and displaying information about specific topics of interest. In certain embodiments, information is processed with advanced computation and analytical techniques in which detailed statistical data is generated and refined to produce meaningful quantitative and qualitative information that may be useful in analyzing the economic performance of specific businesses or geographical regions of interest.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/215,954 filed on Mar. 17, 2014, which claims priority to U.S.Provisional Application No. 61/799,816 filed Mar. 15, 2013, both ofwhich are incorporated by reference herein in their entirety.

OVERVIEW

From time to time, a user may wish to acquire certain detailedinformation about a topic. For purposes of this application, the term“user” collectively describes a person, group, entity, corporation thatwishes to acquire information typically related to trends and analysisbased on information that may be acquired from one or more sources.Also, for purposes of this application, the term “topic” collectivelydescribes a person, group, entity, item, location, event, trend, issue,fact, or anything else about which a user wishes to acquire certaindetailed information. Certain known procedures for acquiring informationincludes searching for or requesting images regarding a topic and thenpermitting human review of the images to identify information in thoseimages. However, known procedures are often limited in thesearch/request parameters by which images may be found. Accordingly,many irrelevant or useless images are returned to the user with onlysome images that are possibly relevant. Such irrelevant or uselessimages must be sorted and reviewed to identify the relevant images.

Some known procedures attempt to deal with irrelevant or useless imagesby providing a feedback mechanism through the use of which additionalimages may be requested in order to increase the sample size. Althoughthe feedback mechanism may provide additional images, the search/requestparameters are still limited in scope. Simply obtaining more picturesdoes not automatically guarantee that the desired images will beproduced.

Another challenge often associated with obtaining information regardinga selected topic includes obtaining information that is locationspecific. Certain known systems address this challenge by setting up arecording device at a specific location and operating such a devicecontinuously, automatically, or remotely. However, if the person wishesto obtain observations regarding a large topic or multiple topics thatare not within the range of a single recording device located at aspecific location, positioning and maintaining multiple recordingdevices may be cost-prohibitive.

Additionally, at times, the full scope of information that a user wishesto obtain regarding a topic is not available from any one resource. Insuch instances, a user may need to access two or more resources thatcontain some information about the topic. While such multiple resourceinformation allows the user to compare, combine, or otherwise synthesizethe information in order to obtain a synthesized result that isaccurate, the user must engage in a verification process for eachacquired piece of information. The result may be quantified in one ormore sets of information that is verified.

Other known systems address the challenge of obtaining desired images byacquiring one or more already-existing image representations of aselected topic. However, already-existing image representations may notdepict the entirety of the desired topic or may not include metadata or,even if they do include metadata, may not include renderable metadata.

Additional known systems may address the challenge of obtaining desiredimage representations by engaging a third party to provide such imagerepresentations that depict a topic. However, a third partyrepresentation of a topic may be incomplete (e.g., only depict a portionof the topic), fuzzy (not high enough resolution), blocked (obstructionblocking desired or complete view of topic), incompatible (images fromdifferent or unknown time periods), or inaccurate (e.g., not depict thetopic at all).

Even if the user obtains one or more images depicting certaininformation about a given topic, there may be additional questions aboutwhether the one or more images are compatible for comparison. Forexample, if a user wishes to compare the holiday season sales of a firstretail store with the holiday season sales of a second retail store, theuser may obtain two sets of information—e.g., (1) number of vehicles inthe parking lot of the first retail store during one day in the holidayseason and (2) number of vehicles in the parking lot of the secondretail store during a day in the holiday season. However, if the vehiclecount at each store did not occur on the same day, the value of thecomparison may be limited.

An additional concern arises in the steps taken to analyze andsynthesize data from image representations of a topic, particularly whenlarge quantities of data is required from multiple image representationsources to provide meaningful statistical information about theparticular topic of interest. Accordingly, steps must be taken toaccount for differences in how and when the information was obtained andanalyzed.

There is a demand for an improved system and methods of identifying,assessing, obtaining, evaluating, processing and displaying informationabout specific topics of interest. The present invention satisfies thisdemand.

SUMMARY OF THE INVENTION

Certain embodiments of the present invention include first identifyingthe objective of the research. The objective may be to find a detailabout one or more topics, answer one or more questions, compareinformation about one or more topics or define other information that anentity wishes to know about one or more topics. The identifying step mayinclude defining a topic at a broad level (e.g., some Subway® franchise)or at a specific level (e.g. Subway® franchise at specific address).

In certain embodiments, only a single topic may be identified (e.g., aretail store parking lot, restaurant parking lot, service providerparking lot, coal pile, landfill, marine port, or construction site, toname a few.). The single topic may be captured at a single time point orat multiple time points (e.g., in relationship to time, multiple timesper minute/hour/day/month/year).

In certain embodiments, any two or more topics may be identified to forma topic collection (also termed a “set of topics” in this application).For example, one topic collection may include multiple locations of thesame retail store (e.g., First Topic=Best Buy® retail store inCalifornia, Second Topic=Best Buy® retail store in Illinois, ThirdTopic=Best Buy® retail store in New York), which permits comparison of achain store in different regions. Another topic collection may includemultiple competing retail chains in a single geographic region (e.g.,First Topic=Petsmart® in Chicago, Ill., Second Topic=Petco® in ChicagoIll., Third Topic=Pet Supplies Plus in Chicago, Ill.). An additionaltopic collection may include multiple competing retail chains inmultiple geographic regions (e.g., First Topic=Walgreens in Texas,Second Topic=CVS® in South Carolina, Third Topic=Rite Aid inPennsylvania). Other topic collections may include clusters of certainretail stores, e.g., regional cluster of representative stores.Additional topic collections may include multiple locations of the sameretail store each having multiple observations, multiple retail storesin multiple regions (e.g., an index), and multiple retail stores at onelocation (e.g., indoor malls).

In certain embodiments, a topic collection may include a random sampleor a complete list of all stores in a specific chain or a franchise. Aplurality of topic collections may be generated, for example, to prepareinformation about sectors of the economy, sectors of industry, or otherlarger groups. Each topic collection may be configured to permitobtaining certain content information efficiently.

Once the objective is identified, one embodiment includes assessingwhether the information can be found from a single known source. If not,the next step may include determining what component information may beuseful to generate the answer. The component information may includeinformation obtained from primary sources and secondary sources.Information from a primary source may be that which may be perceiveddirectly by people or computers. Secondary source information may be allinformation that is perceived in some way other than a directobservation (e.g., while the primary source may be an observation, theobservation was published in a reputable journal, so the second sourceinformation is from a secondary source).

The next step may include obtaining component information. Componentinformation about the topic may require capturing information inmultiple locations or at multiple time points, for example, if certaintopics have multiple components or are mobile. One method to obtain suchinformation is to record the topic for location-remote or time-shiftedviewing using a recording device.

For purposes of this application, a “recording device” may be anymechanical or electronic device configured to generate a visual or audiorecording. A recording device may be configured as a camera or arendering instrument. Examples of a camera include a still camera,digital camera, video camera, webcam, camera integrated with a mobilephone, traffic camera, security camera, satellite camera, aerial mappingcamera, aerial laser measurement (LiDAR), aerial or satellite radarmeasurement (SAR), aerial thermal mapping (heat), pneumatic tubes tomeasure car movements, vehicle-mounted cameras (Google Streetview, orother views generated from car, truck, van, train, helicopter, airplane,space shuttle, or boat, to name a few), or audio recording devices. Arendering instrument may be any instrument by which a person may observecomponent information and document information relevant to the topic. Arendering instrument may rely on human observations related to thetopic. Examples of such instruments are pen, pencil, marker, ink, paper,paint, paintbrush, canvas, surface, tablet, mobile device, stylus,program used to prepare a digital rendering, or other such instruments.

Recorded component information may then be presented as individualsample points, based on a specific time and location. Eachindividualized sample points, for purposes of this application, istermed “representation” and more than one representation is termed“representations”. Representations may depict component information frommultiple time points, multiple location points, or a combination ofmultiple time and location points. Representations may also includecomponent information from one or more primary sources, one or moresecondary sources, or a combination of component information from one ormore primary sources and one or more secondary sources.

In certain embodiments, the representations may be configured to depict,for example, coal stock piles or landfills from which volume informationmay be calculated and compliance information or environmentalinformation may be determined. Certain embodiments includerepresentations configured to depict, for example, progress of aconstruction site which may permit off-site or remote monitoring andcompliance with deadlines.

In certain embodiments, representations may depict onshore and offshoreoil wells, onshore and offshore gas wells, factory parking lots whichpermit measuring employee traffic to predict factory output, commutertransportation parking lots which may permit assessing rail/bus use totrack employment of the broader economy and regional transportationneeds, factory inventory yards, wind turbines, car lots, constructionmachinery especially large machinery that gets produced and storedoutside, large utility scale solar panel projects (e.g., First Solarprojects in Mojave Desert), mines including pit activity, waste piles,and leach ponds, agricultural areas which may permit predicting size ofharvest and/or health of the crops, ports which may permit monitoringshipping containers, commodities piles, and overall activity, hospitalparking lots to track patient utilization, power plants to trackactivity including raw material resources including coal piles, dams,bridges, highways, or toll booths.

In certain embodiments of the present invention, a search or request fora selection of representations generated from a camera such as asatellite camera may be prepared. The search or request element mayinclude criteria by which the results are refined. Such criteria mayinclude resolution, pixilation, cloud cover, date of creation, date ofmodification, time of day of creation, type of camera, zoom in or zoomout, completeness of coverage of topic, removal of duplicate portions oftopic, perspective from which representation was captured, or otherconditions which were configured to maximize the relevance of theresulting selection of representations.

Certain embodiments of the present invention may include acquiring oneor more representations that include renderable metadata such that themetadata may be automatically extracted or readily extractable from therepresentation file.

In certain embodiments of the present invention, the purpose ofobtaining one or more representations may be for evaluating specificcharacteristics found within the one or more representations—termed“selected content” or “content information”—from which certaininformation may be further processed. For example, in certainembodiments, the representations may be configured to depict retailparking lots from which specific economic information or commercialinformation about the associated retail stores may be estimated.

One embodiment allows the criteria to be selected by which the one ormore representations for content information may be evaluated, examplesof such criteria include but are not limited to defining a specific timepoint and economic baseline for a particular retail chain in a given zipcode. A determination may then be made whether or not such minimumbaseline or threshold was met by a statistical evaluation of quantifiedinformation using the provided representations. If the minimum thresholdis not met, then the representation may be removed from the data set ofcontent information. If the minimum threshold was met, then therepresentation may be retained within the data set for furtherevaluation of the content information.

Additional certain embodiments for evaluating the one or morerepresentations may include criteria by which representations may beselected including adequate image resolution, tolerable level of cloudcover or other obstructions, certain level of completeness, and whetherthe desired topic is illustrated as desired. In certain embodiments, theselection of representations may be evaluated for appropriate andaccurate coverage of the topic.

In certain embodiments, content information may be extracted from a setof representations. Content information may be extracted manually by ahuman reviewing the representation, extracted automatically by acomputer system, or extracted by a combination of human and systemreview. In certain embodiments, the extracted content information may beexported to a document, notification, or other output. Also, theextracted information may be further evaluated based on relevance to theachieving the research objective.

In certain embodiments, content information may be extracted from therepresentations by the user reviewing the representation and documentingcontent from observing the representation. Documenting information mayinclude counting cars in a parking lot, counting empty spaces in aparking lot, dividing number of cars by number of spaces to determine afill rate, area measurements of a store or parking lot or topic,counting “share of cars” at competing retailers (total cars at retailerA vs. total cars at retailer A and retailer B), counting boats in aport, counting semi-trailers at a delivery dock, changes since last datea representation was captured (e.g., store parking lot expansion orremodel, store expansion or remodel), changes in number of items,describing state of a construction project, or making other observationsabout that which is depicted in the representation. The contentinformation may be also integrated into an information storagecomponent.

In other embodiments, the user may specifically direct the system toextract the metadata. Metadata may include the time and date of creationof the representation, time and date of last edit of the representation,satellite information, footprint of the image collection as related tothe geographic boundaries of topic, product type, and other informationsaved in association with the representation. Examples of a “producttype” include coarsely orthorectified panchromatic or 3-band naturalcolor imagery in GeoTIFF or JPEG2000 format in a UTM WGS84 Meterscoordinate system. The automatic or user-implemented extraction ofmetadata may result in a metadata output. The metadata output may beconfigured to be automatically integrated into a content informationinterface or configured to be uploaded to an information storagecomponent upon order by the user.

In certain embodiments, specific content information, for example,metadata, may be automatically extracted from the representation uponreceipt of the representation in the user's system.

In certain embodiments, if a certain threshold of irrelevantrepresentations is presented, the system may dispatch an invitation tofurther refine the active criteria in the search/request component in aneffort to improve the resulting representations and content informationrather than automatically remove such representations from the data setof content information.

In certain embodiments, after evaluating the one or more representationsfor content information depicting a topic or topic collection, thecontent information may undergo one or more processing steps. Theprocessing steps may include defining subtopics of interest from thecontent information, calculating statistics related to the contentinformation subtopic, balancing the content information subtopic, andsynthesizing the content information subtopic.

In certain embodiments, a processing step of the content information asa subtopic may include overlaying coordinate units to the contentinformation to detect which coordinate units intersect with a subtopicor a component of a subtopic. The coordinate units that do not intersectwith a subtopic or a component of a subtopic optionally may beeliminated from the content information or labeled in some manner. Thecoordinate units that do intersect with a subtopic or a component of asubtopic may be further examined. For example, for cloud cover,coordinate units that have, for example, more than 95% cloud cover maybe purged from the representation collection. The remaining coordinateunits may be further pared down based on resolution parameters. Forexample, representations having an average resolution of less than 0.95panchromatic may be chosen.

In certain embodiments, the processing steps of the content informationas a subtopic may be repeated for additional content information from asecond set of representations or representation collections. Forexample, a first processing cycle may process representations fromretail stores in January 2012 and a second processing cycle may processrepresentations for retail stores in January 2013 to permit a 2012-2013comparison.

One embodiment of a subtopic of interest that may utilize the processingstep is a yearly comparison of content information related to one ormore retail stores, selected dates for year-over-year comparable periodsof time, and specific environmental parameters, such as cloud coveragefor certain time periods with respect to specific geographicallocations.

One embodiment of a subtopic of interest that may utilize the processingstep is the identification of all content information for businessesthat utilize GIS.

One embodiment of a subtopic of interest that may utilize the processingstep is a compilation of all imagery that intersects with allrepresentations within the content information. Such imagery mayinclude, but is not limited to, that regarding a previous selectionsub-select data, and which has a cloud cover of less than 70% across thefull image, or which has an average panchromatic resolution of less than0.85 centimeters per pixel, or which matches the correct dates neededbased on the question.

One embodiment of a processing step of the content information as asubtopic may include writing selections to new features and combiningfeatures into a single feature.

One embodiment of a processing step of the content information as asubtopic may include dissolving certain features based on spatialoverlap and common fields including, but not limited to, “Ticker”,“Address”, “State”, “Store_OID”, “Zip”, “CATALOGID”, “ACQDATE”,“AVPANRES”, “BROWSEURL”, “CLOUDCOVER”, “Latitude”, and “Longitude”.

One embodiment of a processing step of the content information as asubtopic may include sorting features by panchromatic resolution.

One embodiment of a processing step of the content information as asubtopic may include removing duplicate observations, such as those ofretail stores at the same time point.

One embodiment of a processing step of the content information as asubtopic may include dissolving a second time based on common fieldsfound in imagery metadata including, but not limited to, “CATALOGID”,“ACQDATE”, “AVPANRES”, “BROWSEURL”, “CLOUDCOVER”.

One embodiment of a processing step of the content information as asubtopic may include finding and deleting oversampled observations. Suchoversampled observations may include, but are not limited to, largecorporate retail entities, such as AutoZone®, Starbucks®, Walmart®, etc.

One embodiment of a processing step of the content information as asubtopic may include calculating the area of each remainingfeature/observation.

One embodiment of a processing step of the content information as asubtopic may include detecting and removing observations that are lessthan a certain geographical quantity, such as 2 square kilometers, andthat may be visually distorted by a certain quantity, such as cloudovercast by greater than 40% of the geographical region of theobservation.

One embodiment of a processing step of the content information as asubtopic may include ascertaining and deleting all observations that areless than a certain geographical quantity, such as less than 1.5 squarekilometers.

One embodiment of a processing step of the content information as asubtopic may include buffering all observations under a specificgeographical quantity, such as 3 square kilometers.

One embodiment of a processing step of the content information as asubtopic may include counting all remaining features by date ofacquisition.

Certain embodiments of a processing step of the content informationinclude one or more balancing steps of the content information subtopic.The one or more balancing steps of the content information subtopic mayinclude eliminating outliers, reducing imbalance from over or undersampling, randomizing and simulating multiple possible final datasets bysystematically removing the imbalances in a random way or systematic waymultiple times, weighting data points according to relevance, andapplying coarsened exact matching statistical analysis.

In certain embodiments, the balancing steps may include procuring anappropriate sample size, correcting for oversampling or under sampling,or eliminating outliers, among other things.

In certain embodiments, the balancing steps may be conductedautomatically by a computer system. In other embodiments, a human maymanage one or more of the balancing steps.

In certain embodiments, a program may be expressed in a language such,as a script in the “R” programming language, and may be configured toautomatically run representations through the selected balancing stepsand record results for further analysis and evaluation.

In certain embodiments, the balancing steps may be based on the numberof trails based on user input.

In certain embodiments, the balancing steps may be based on parking lotdata, including the size and shape of the parking lot to definecapacity.

In certain embodiments, the balancing steps may be based on input valuesof time, including overall time, from hours to days to weeks to years,and specific time, including days of the week or hours in the day.

In certain embodiments, the balancing steps may be based on input valuesof geographical location and information pertaining to the specificgeographical location, such as capacity utilized within the geographicallocation.

In certain embodiments, the processing steps also may include generatingsynthesized information from a number of different combinations ofcontent information subtopics. For example, if the content informationfrom a group of representations includes a number of figures that may ormay not be considered outliers by certain statistical methods, a numberof synthesis steps may be conducted. For example, if the figures includeA, B, C, and D, a first synthesis step may include synthesizing allfigures A, B, C, and D. A second synthesis step may include synthesizingfigures A, B, C, but not D (if, for example, D is an outlier under thequartile method). A third synthesis step may include synthesizing allfigures A, B, D, but not C (if, for example, C is an outlier under thez-score method).

Additional processing steps may calculate the synthesized informationbased on other statistical methods. The user may choose one of thesynthesized information sets as the final synthesized information or maycombine or compare two or more synthesized information sets.

Certain embodiments of the processing step of the content information asa subtopic may include sending all remaining features to an imagingcompany, for example, a satellite company for order placement.

In certain embodiments of the processing step of the content informationas a subtopic may include organizing content information as a subtopicbased on the type and scope of synthesized subtopic content information.For example, if the synthesized information desired is the relativeeconomics of pet stores in the Midwest Region of the United States overa one month time period in 2012 and a one month time period in 2013, allof the relevant entries from the subtopic representation collections maybe entered into the content information dataset.

Certain embodiments also may permit forming supplemental synthesizedinformation. For example, if a retail traffic result for a specificchain store is the first synthesized information and a retail trafficresult for a specific chain store is the second synthesized information,a combination of the first retail traffic result and second retailtraffic result is an index of traffic results—or supplementalsynthesized information. Such an index may be formed by identifying aretail traffic result for big retailers (e.g., WalMart®, Home Depot®,Lowes®, McDonald's®) and weighting each retail traffic result by UnitedStates Revenue to correlate with the Bureau of Economic AnalysisConsumer Spending Index.

Another example index embodiment may include a semiconductor indexconfigured to track employment at semiconductor manufacturing andpackaging plants around the world. Advantageously, certain embodimentsof the present invention utilize representations derived from camerasmounted on satellites. Because such satellites orbit the Earth at agreat distance, they may efficiently capture representations related tomultiple topics without requiring a permanent camera positioned relativeto the topic and without requiring a person wielding a camera to visitthe topic for every observation.

Another example of synthesized content information of certain subtopicsare population statistics regarding certain regions, e.g., towns,villages, cities, counties, states, all of which may include totalnumber of people, number of females or males, population density,population growth predictions, average family size, age distribution ofpopulation, education level of population, income level for population,disposable income for population, race distribution of population,average number of people per household, and average number of cars perhousehold. In other embodiments, synthesized information includes coalstorage statistics, legal compliance requirements for landfills, orother information not derived from a representation.

In certain embodiments, the synthesized information may includeinformation about: fill rates of parking lots; market share percentageof a particular retail store in geographic location; consumer confidencelevels; trends in retail store revenues over time or over region;composition of same-store sales, same-store transactions, or same-storetraffic at one or more retailers; market share shifts in one or moregeographic locations at one or more retailers which may be measured as“share of cars” over a specific time period (total cars at retailerA/total cars at retailer A+retailer B+retailer N . . . ); conversionrates or “close rates” of cars in the parking lot versus actualtransactions measured by the store itself (e.g., number of transactionsin +/−15 minute period surrounding the timestamp of the image divided bythe number of cars measured in the representation); employment trendsfor the economy as a whole or for a specific company;production/inventory trends for a specific company; historical traffictrends to monitor geographical “hot spots” for new store development; oreffectiveness of a promotion, advertisement, remodeling project, orother occurrence. Other examples of synthesized information may includewhether and for how long certain ships are docking in a port, quantityof coal in a coal pile, volume of waste in a landfill.

In other embodiments, synthesized information includes economiccomparable topics by calculating the sum of growth, average growth, andshopper conversion rate growth. The resulting synthesized informationmay be configured to efficiently address the defined question.

In certain embodiments, the synthesized information may then bedisplayed to achieve the objective of the research. Certain embodimentsof the present invention may require formatting of the synthesizedinformation such that it may be sent to another entity. The formattingmay include producing a report having numeric displays and optionallygraphical displays of the synthesized information.

One advantage of certain embodiments of the present invention may permitmaximizing the relevance of the selection of representations obtained.

Another advantage of certain embodiments of the present invention maypermit verifying the significance of the selection of representationsobtained.

Another advantage of certain embodiments of the present invention mayeliminate or reduce the number of feedback loops needed (relative toknown procedures) since the process is refined from the earliersearch/request steps.

Another advantage of certain embodiments of the present invention mayenhance comparability of content extracted from representations bybalancing the content information,

Another advantage of certain embodiments of the present invention mayefficiently generate synthesized information about topics that may bedifficult to observe from a non-aerial perspective.

Another advantage of certain embodiments of the present invention mayefficiently generate synthesized information with increased accuracy.

The present invention and its attributes and advantages will be furtherunderstood and appreciated with reference to the detailed descriptionbelow of presently contemplated embodiments, taken in conjunction withthe accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The preferred embodiments of the invention will be described inconjunction with the appended drawings provided to illustrate and not tothe limit the invention, where like designations denote like elements,and in which:

FIG. 1A illustrates one embodiment of the system according to thepresent invention;

FIG. 1B illustrates one embodiment of he evaluation steps of the systemaccording to the present invention;

FIG. 1C illustrates one embodiment of the processing steps of the systemaccording to the present invention;

FIG. 1D illustrates an embodiment of additional processing steps of thesystem according to the present invention;

FIG. 1E illustrates an embodiment of balancing steps of the systemaccording to the present invention;

FIG. 2A illustrates an exemplary computer system,

FIG. 2B illustrates a cloud computing system;

FIG. 3A illustrates a certain embodiment of a report according to thepresent invention;

FIG. 3B illustrates a certain embodiment of components of a reportaccording to the present invention;

FIG. 3C illustrates a certain embodiment of components of a reportaccording to the present invention;

FIG. 3D illustrates a certain embodiment of components of a reportaccording to the present invention;

FIG. 3E illustrates a certain embodiment of components of a reportaccording to the present invention;

FIG. 4A illustrates a certain embodiment of components of a reportaccording to the present invention;

FIG. 4B illustrates a certain embodiment of components of a reportaccording to the present invention;

FIG. 4C illustrates a certain embodiment of components of a reportaccording to the present invention;

FIG. 4D illustrates a certain embodiment of components of a reportaccording to the present invention;

FIG. 5A illustrates a certain embodiment of a representation;

FIG. 5B illustrates a certain embodiment of a representation;

FIG. 5C illustrates a certain embodiment of a representation;

FIG. 5D illustrates a certain embodiment of a representation; and

FIG. 6A illustrates a certain embodiment of a report or component of areport, as displayed as a combination of a representation andstatistical information related to specific topics determined by theuser input.

FIG. 6B illustrates a certain embodiment of a report or component of areport, as displayed as a combination of a representation andstatistical information related to specific topics determined by theuser input.

FIG. 6C illustrates a certain embodiment of a report or component of areport, as displayed as a combination of a representation andstatistical information related to specific topics determined by theuser input.

FIG. 6D illustrates a certain embodiment of a report or component of areport, as displayed as a combination of a representation andstatistical information related to specific topics determined by theuser input.

FIG. 6E illustrates a certain embodiment of a report or component of areport, as displayed as a combination of a representation andstatistical information related to specific topics determined by theuser input.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

FIGS. 1A through FIG. 1E illustrate certain embodiments according to thepresent invention.

In FIG. 1A, one of the preferred embodiments according to the presentinvention is illustrated. The user may first identify an objective ofresearch 12 and assess sources of component information to achieve theresearch objective 14. One or more representations related to the topicmay be obtained from the available component information 16. The one ormore representations may then be evaluated for content information 18.The content information from the one or more representations may beprocessed 20. The processed content information may then be displayed toachieve the objective of the research 22.

FIG. 1B illustrates one preferred embodiment according to the presentinvention of the one or more evaluation steps 30 of the system 10according to the present invention. When the one or more representationsmay be evaluated for content information 18, specific criteria may beselected to evaluate the one or more representations 32. The selectedcriteria may be defined as having met or not a minimum threshold 34. Therepresentations that do not meet a minimum threshold of selectedcriteria may be removed from the data set of representations 36. Theremaining representations may be extracted as content information 38.

FIG. 1C illustrates one preferred embodiment of the one or moreprocessing steps 40 of the system 10 according to the present invention.When the content information from the one or more representations isprocessed 20, the content information may be defined as one or moresubtopics of interest 42. Statistics related to the content informationsubtopics is then calculated 44. The content information from thesubtopics is then balanced 46. The subtopic content information is thenfurther synthesized based on user input 48.

FIG. 1D illustrates a preferred embodiment of the one or more additionalprocessing steps 50 of the system 10 according to the present invention.When the content information from the one or more representations isprocessed 20, the content information may be defined as one or moresubtopics of interest 42. The content information of the subtopics maythen be overlaid with coordinate units to define similar features 54.The one or more similar features of the content information may then bewritten into selections 56. The selections may then be quantitativelysorted and combined 58. The duplicate or undesired information may thenbe removed from the selections 60. The statistics of the selections, asdefined by the user, are then calculated 62.

FIG. 1E illustrates a preferred embodiment of the one or more balancingmethod 70 from the processing step of balancing the content information46 according to the present invention. The content information may beconverted into Array 1 74. Statistics may then be calculated from Array1 76. Array 1 statistics may then be then split into an Outlier Arrayand Non-outlier Array if the standard deviation balancing is selected78. If the standard deviation balancing is not selected, then Array 1may remain unbalanced, does not split into an Outlier Array andNon-outlier Array and is converted, as is, to Array 2 82. If Array 1 issplit, the Outlier Array may be balanced year-over-year such as byregion of interest 80. The Non-outlier Array may remain unbalanced. Thebalanced Outlier Array and Non-outlier Array may then be combined intoArray 2 82. A secondary balancing step is then performed on Array 2based on user input factors, and is transposed into Array 3 84. Suchuser input factors may include, but are not limited to, averagingobservations per day by region. Array 3 may then be buffed based on userinput factors into Output Array 4 86. Such user input factors mayinclude, but are not limited to, year-over-year by region, week, and dayof week as well as calculating the overall and regional averages ofOutput Array 4. Output Array 4 is then displayed for further synthesis88.

FIG. 2A illustrates an exemplary computer system 200 that may be used toimplement the methods according to the present invention. One or morecomputer systems 200 may carry out the methods presented herein ascomputer code.

Computer system 200 includes an input/output display interface 202connected to communication infrastructure 204—such as a bus—, whichforwards data such as graphics, text, and information, from thecommunication infrastructure 204 or from a frame buffer (not shown) toother components of the computer system 200. The input/output displayinterface 102 may be, for example, a keyboard, touch screen, joystick,trackball, mouse, monitor, speaker, printer, any other computerperipheral device, or any combination thereof, capable of enteringand/or viewing data.

Computer system 200 includes one or more processors 206, which may be aspecial purpose or a general-purpose digital signal processor thatprocesses certain information. Computer system 100 also includes a mainmemory 208, for example random access memory (“RAM”), read-only memory(“ROM”), mass storage device, or any combination of tangible,non-transitory memory. Computer system 200 may also include a secondarymemory 210 such as a hard disk unit 212, a removable storage unit 214,or any combination of tangible, non-transitory memory. Computer system200 may also include a communication interface 216, for example, amodern, a network interface (such as an Ethernet card or Ethernetcable), a communication port, a PCMCIA slot and card, wired or wirelesssystems (such as Wi-Fi, Bluetooth, Infrared), local area networks, widearea networks, intranets, etc.

It is contemplated that the main memory 208, secondary memory 210,communication interface 216, or a combination thereof, function as acomputer usable storage medium, otherwise referred to as a computerreadable storage medium, to store and/or access computer softwareincluding computer instructions. For example, computer programs or otherinstructions may be loaded into the computer system 200 such as througha removable storage device, for example, a floppy disk, ZIP disks,magnetic tape, portable flash drive, optical disk such as a CD or DVD orBlu-ray, Micro-Electro-Mechanical Systems (“MEMS”), nanotechnologicalapparatus. Specifically, computer software including computerinstructions may be transferred from the removable storage unit 214 orhard disc unit 212 to the secondary memory 210 or through thecommunication infrastructure 204 to the main memory 208 of the computersystem 200.

Communication interface 216 allows software, instructions and data to betransferred between the computer system 200 and external devices orexternal networks. Software, instructions, and/or data transferred bythe communication interface 216 are typically in the form of signalsthat may be electronic, electromagnetic, optical or other signalscapable of being sent and received by the communication interface 216.Signals may be sent and received using wire or cable, fiber optics, aphone line, a cellular phone link, a Radio Frequency (“RF”) link,wireless link, or other communication channels.

Computer programs, when executed, enable the computer system 200,particularly the processor 206, to implement the methods of theinvention according to computer software including instructions.

The computer system 200 described herein may perform any one of, or anycombination of, the steps of any of the methods described herein. It isalso contemplated that the methods according to the present inventionmay be performed automatically, or may be accomplished by some form ofmanual intervention.

The computer system 200 of FIG. 2A is provided only for purposes ofillustration, such that the invention is not limited to this specificembodiment. It is appreciated that a person skilled in the relevant artknows how to program and implement the invention using any computersystem.

The computer system 200 may be a handheld device and include anysmall-sized computer device including, for example, a personal digitalassistant (“PDA”), smart hand-held computing device, cellular telephone,or a laptop or netbook computer, hand held console or MP3 player,tablet, or similar hand held computer device, such as an iPad®, iPadTouch® or iPhone®.

FIG. 2B illustrates an exemplary cloud computing system 220 that may beused to implement the methods according to the present invention. Thecloud computing system 220 includes a plurality of interconnectedcomputing environments. The cloud computing system 220 utilizes theresources from various networks as a collective virtual computer, wherethe services and applications can run independently from a particularcomputer or server configuration making hardware less important.

Specifically, the cloud computing system 220 includes at least oneclient computer 222. The client computer 222 may be any device throughthe use of which a distributed computing environment may be accessed toperform the methods disclosed herein, for example, a traditionalcomputer, portable computer, mobile phone, personal digital assistant,tablet to name a few. The client computer 222 includes memory such asrandom access memory (“RAM”), read-only memory (“ROM”), mass storagedevice, or any combination thereof. The memory functions as a computerusable storage medium, otherwise referred to as a computer readablestorage medium, to store and/or access computer software and/orinstructions.

The client computer 222 also includes a communications interface, forexample, a modem, a network interface (such as an Ethernet card), acommunications port, a PCMCIA slot and card, wired or wireless systems,etc. The communications interface allows communication throughtransferred signals between the client computer 222 and external devicesincluding networks such as the Internet 224 and cloud data center 226.Communication may be implemented using wireless or wired capability suchas cable, fiber optics, a phone line, a cellular phone link, radio wavesor other communication channels.

The client computer 222 establishes communication with the Internet204—specifically to one or more servers—to, in turn, establishcommunication with one or more cloud data centers 226. A cloud datacenter 226 includes one or more networks 230 a, 230 b, 230 c managedthrough a cloud management system 228. Each network 230 a, 230 b, 230 cincludes resource servers 232 a, 232 b, 232 c, respectively. Servers 232a, 232 b, 232 c permit access to a collection of computing resources andcomponents that can be invoked to instantiate a virtual computer,process, or other resource for a limited or defined duration. Forexample, one group of resource servers can host and serve an operatingsystem or components thereof to deliver and instantiate a virtualcomputer. Another group of resource servers can accept requests to hostcomputing cycles or processor time, to supply a defined level ofprocessing power for a virtual computer. A further group of resourceservers can host and serve applications to load on an instantiation of avirtual computer, such as an email client, a browser application, amessaging application, or other applications or software.

The cloud management system 228 can comprise a dedicated or centralizedserver and/or other software, hardware, and network tools to communicatewith one or more networks 230 a, 230 b, 230 c, such as the Internet orother public or private network, with all sets of resource servers 232a, 232 b, 232 c. The cloud management system 228 may be configured toquery and identify the computing resources and components managed by theset of resource servers 232 a, 232 b, 232 c needed and available for usein the cloud data center 226. Specifically, the cloud management system228 may be configured to identify the hardware resources and componentssuch as type and amount of processing power, type and amount of memory,type and amount of storage, type and amount of network bandwidth and thelike, of the set of resource servers 232 a, 232 b, 232 c needed andavailable for use in the cloud data center 226. Likewise, the cloudmanagement system 228 can be configured to identify the softwareresources and components, such as type of Operating System (“OS”),application programs, and the like, of the set of resource servers 232a, 232 b, 232 c needed and available for use in the cloud data center226.

The present invention is also directed to computer products, otherwisereferred to as computer program products, to provide software to thecloud computing system 220. Computer products store software on anycomputer useable medium, known now or in the future. Such software, whenexecuted, may implement the methods according to certain embodiments ofthe invention. Examples of computer useable mediums include, but are notlimited to, primary storage devices (e.g., any type of random accessmemory), secondary storage devices (e.g., hard drives, floppy disks, CDROMS, ZIP disks, tapes, magnetic storage devices, optical storagedevices, Micro-Electra-Mechanical Systems (“MEMS”), nanotechnologicalstorage device, etc.), and communication mediums (e.g., wired andwireless communications networks, local area networks, wide areanetworks, intranets, etc.). It is to be appreciated that the embodimentsdescribed herein may be implemented using software, hardware, firmware,or combinations thereof.

The cloud computing system 220 of FIG. 2B is provided only for purposesof illustration and does not limit the invention to this specificembodiment. It is appreciated that a person skilled in the relevant artknows how to program and implement the invention using any computersystem or network architecture.

FIG. 3A-FIG. 3D illustrate certain of the specific embodiments of thereports or components of reports that may be generated through the useof the present invention. FIG. 3A illustrates one embodiment of theoverall Analysis Dashboard 300. Through dashboard 300, a user may inputthe desired minimum and maximum data range for the representationsanalyzed 302. The current data range may be, for example, the size ofthe minimum and maximum number of parking spaces contained within thesample 304. A time period may be selected by a user for the report 306.The associated statistics of the subtopic for the selected time andlocation indicated may be then reported 308.

FIG. 3B illustrates one of the many specific embodiments of a componentof a report 320 that may be generated through the use of the presentinvention. The report may be configured to provide specific statistics322, 324 that may be associated with particular subtopics of interest326 for a given sample number or representations analyzed 323, 325.

FIG. 3C illustrates another specific embodiment of a component of areport 330 that may be generated through the use of the presentinvention. The component of the report 330 is another example ofspecific statistics 332, 334 associated with particular subtopics ofinterest 326.

FIG. 3D illustrates another specific embodiment of a component of areport 340 that may be generated through the use of the presentinvention. The component of the report 340 is another example ofspecific statistics 342, 344 associated with particular subtopics ofinterest 346.

FIG. 3E illustrates a specific embodiment of a component of a report350. The component of the report 350 is another example of specificstatistics 352, 354 associated with particular subtopics of interest356,

FIG. 4A-FIG. 4D illustrate other embodiments of reports or components ofreports that may be generated through the use of the present invention.The illustrated reports are displayed as graphical representations ofinformation related to specific topics determined by the user input.FIG. 4A illustrates a specific embodiment of components of a report 400,as graphically displayed to show specific statistics associated withparticular subtopics of interest. FIG. 4B illustrates a specificembodiment of components of a report 410, and provides specificstatistics associated with particular subtopics of interest. FIG. 4Cillustrates a specific embodiment of components of a report 420, andprovides specific statistics associated with particular subtopics ofinterest. FIG. 4D illustrates a specific embodiment of components of areport 430, and provides specific statistics associated with particularsubtopics of interest.

FIG. 5A-FIG. 5D illustrate some of the representations of the certaincontent information that may be obtained for further evaluation.

FIG. 5A illustrates an example of a representation in which contentinformation may be obtained 500. FIG. 5B illustrates an example of arepresentation in which content information may be obtained 510. FIG. 5Cillustrates an example of a representation in which content informationmay be obtained 520. FIG. 5D illustrates an example of a representationin which content information may be obtained 530.

FIG. 6A-6E illustrates example embodiments of reports or components ofreports that may be generated through the use of the present invention.The illustrations show displays of a combination of representations andstatistical information related to specific topics and subtopicsdetermined by the user input.

FIG. 6A illustrates an example embodiment of a report or component of areport 600, as displayed as a combination of a representation 602 andstatistical information 604 related to specific topics and subtopicsdetermined by the user input. In the report 600, specific componentinformation is marked 606.

FIG. 6B illustrates an example embodiment of a report or component of areport 610, as displayed as a combination of a representation 612 andstatistical information 614 related to specific topics and subtopicsdetermined by the user input. In the report 610, specific componentinformation is marked 616.

FIG. 6C illustrates an example embodiment of a report or component of areport 620, as displayed as a combination of a representation 622 andstatistical information 624 related to specific topics and subtopicsdetermined by the user input. In the report 620, specific componentinformation is marked 626.

FIG. 6D illustrates an example embodiment of a report or component of areport 630, as displayed as a combination of a representation 632 andstatistical information 634 related to specific topics and subtopicsdetermined by the user input.

FIG. 6E illustrates an example embodiment of a report or component of areport 640, as displayed as a representation 642 with specific componentinformation marked 626.

While the disclosure is susceptible to various modifications andalternative forms, specific exemplary embodiments of the presentinvention have been shown by way of example in the drawings and havebeen described in detail. It should be understood, however, that thereis no intent to limit the disclosure to the particular embodimentsdisclosed, but on the contrary, the intention is to cover allmodifications, equivalents, and alternatives falling within the scope ofthe disclosure as defined by the appended claims.

What is claimed is:
 1. A computer system for determining an economicperformance of a business location comprising the steps of: a recordingdevice capturing one or more visual recordings of a parking lot of thebusiness location; a processor in communication with the recordingdevice, the processor including instructions that when executed causethe processor to: receive the one or more visual recordings and generateone or more representations from the one or more visual recordings;determine whether a threshold value of a selected criteria is met byeach of the one or more representations, wherein the selected criteriais cloud cover; remove the one or more representations that do not meetthe threshold value; extract content for evaluation from the one or morerefined representations; refine content, wherein the content is refinedusing cloud cover criteria by overlaying coordinate units on the contentto detect the coordinate units that intersect with the cloud covercriteria and removal of the content with the coordinate units that donot intersect with the cloud cover criteria; evaluate the content bycounting a number of parking spaces in a parking lot of the businesslocation; counting a number of vehicles in the parking lot of thebusiness location; and dividing the number of vehicles in the parkinglot by the number of parking spaces in the business location to obtain atraffic result, the traffic result comprising an estimate of economicactivity of the business location; and a display, the display comprisinga plurality of data screen configured to display the business locationby a ticker name, the traffic result, and a time period to which thetraffic result corresponds, the time period including a time slot duringa day, a day of a week, one or more months, or one or more years.
 2. Thecomputer system for determining economic performance of a businesslocation according to claim 1, wherein the traffic result includes oneor more selected from the group comprising: a fill rate of a parkinglot, a market share percentage of a particular business location ingeographic location, a trend in business location revenues over time orover a geographic region, a value of sales of one or more businesslocations, a value of the share of vehicles over a specific time period,a close rate of vehicles in a parking lot of a business location versusactual transactions measured by the business location.
 3. The computersystem for determining economic performance of a business locationaccording to claim 1, wherein the processor further compares two or morerepresentations to obtain the traffic result.
 4. The computer system fordetermining economic performance of a business location according toclaim 1, wherein the recording device is one or more selected from thegroup comprising a camera, a satellite camera, an aerial mapping camera.5. The computer system for determining economic performance of abusiness location according to claim 1, wherein the processor combinesthe traffic result with one or more second traffic results to obtain anindex of traffic results.
 6. The computer system for determiningeconomic performance of a business location according to claim 1,wherein the processor weights the traffic result to correlate with theBureau of Economic Analysis' Consumer Spending Index.
 7. The computersystem for determining economic performance of a business locationaccording to claim 1, wherein the selected criteria further includesresolution and the processor determines whether the threshold value ismet by each of the one or more representations and further removes theone or more representations that do not meet the threshold value.
 8. Thecomputer system for determining economic performance of a businesslocation according to claim 7, wherein the one or more representationsthat have less than 0.95 panchromatic resolution are removed.
 9. Thecomputer system for determining economic performance of a businesslocation according to claim 1, wherein the selected criteria furtherincludes pixilation and the processor determines whether the thresholdvalue is met by each of the one or more representations and furtherremoves the one or more representations that do not meet the thresholdvalue.
 10. The computer system for determining economic performance of abusiness location according to claim 9, wherein the one or morerepresentations that have less than 0.85 centimeters per pixel areremoved.
 11. The computer system for determining economic performance ofa business location according to claim 1, wherein the one or morerepresentations that have more than 95% cloud cover are removed.
 12. Thecomputer method for determine economic performance of a businesslocation according to claim 1, wherein the business location is one ormore of a manufacturing facility, a packaging facility, a retail store,a distribution center, a commuter transportation facility, a recyclingfacility, a construction site, or a power plant.
 13. The computer methodfor determine economic performance of a business location according toclaim 1, further comprising comparing the traffic result to a historicalaverage of a traffic result of the time period to produce a comparison,and the comparison displayed on the display.
 14. A computer method fordetermining an economic performance of a business location comprisingthe steps of: capturing by a recording device one or more visualrecordings of a geographic location, wherein the geographic location isa parking lot; sending by the recording device to a processor the one ormore visual recordings; generating by the processor one or morerepresentations from the one or more visual recordings; refining by theprocessor the one or more representations based on one or more criteria;extracting by the processor content for evaluation from the one or morerefined representations; evaluating by the processor the content toobtain a result, wherein the evaluating step further comprises the stepsof: converting the content into a first array; calculating one or morestatistics using the first array to produce a first array statistic;splitting the first array statistic into an outlier array and anon-outlier array; balancing the outlier array; combining the balancedoutlier array and the non-outlier array into a second array; balancedthe second array to produce a final statistic; and displaying the finalstatistic on a display, wherein the final statistics comprises theeconomic performance of the business location.
 15. The computer methodfor determining economic performance of a business location according toclaim 14, wherein the evaluating step further comprises one or moresteps selected from the group: eliminating one or more outliers of thecontent, correcting over sampling or under sampling of the content,procuring an appropriate sample size of the content, removing randomlyan imbalance of the content, weighting the content according torelevance.
 16. The computer method for determining economic performanceof a business location according to claim 15, wherein the statisticalmethod is one selected from the group of: a quartile method, a z-scoremethod, and a coarsened exact matching method.
 17. A computer method fordetermining an economic performance of a business location comprisingthe steps of: capturing by a recording device during a selected daterange both a first visual recording of the business location and one ormore second visual recordings of other business locations, each visualrecording of a geographic location; sending by the recording device to aprocessor the first visual recording and the one or more second visualrecordings; generating by the processor one or more representations fromthe first visual recording and the one or more second visual recordings;refining by the processor the one or more representations based on oneor more criteria; extracting by the processor content for evaluationfrom the one or more refined representations; evaluating by theprocessor the content to obtain a result, wherein the evaluating stepfurther comprises the steps of: counting by the processor a number ofvehicles in a parking lot of the business location; counting by theprocessor a number of vehicles in a parking lot of each of the otherbusiness locations; and dividing by the processor the number of vehiclesof the business location by a sum of both the number of vehicles of thebusiness location and the number of vehicles of each of the otherbusiness locations to determine a value; and displaying the result on adisplay, wherein the result comprises the economic performance of thebusiness location.
 18. The computer method for determining economicperformance of a business location according to claim 17, wherein therefining step further comprises the steps of: determining whether athreshold value of a selected criteria is met by each of the one or morerepresentations, wherein the selected criteria is one or more selectedfrom the group comprising resolution, pixilation, cloud cover; andremoving the one or more representations that no not meet the thresholdvalue.
 19. The computer method for determining economic performance ofbusiness location according to claim 17, wherein the recording device isone or more selected from the group comprising a camera, a satellitecamera, an aerial mapping camera.
 20. The computer method fordetermining economic performance of a business location according toclaim 17, wherein the one or more representations includes renderablemetadata comprising a time and a date of the capturing step.